In our last guide<\/a>, you built your first “Core Product Health” dashboard in Amplitude<\/a><\/strong>. You can now see what<\/em> is happening in your product at a glance: your weekly active users, your key funnel conversion rates, and your new user retention curve. This is a critical first step, providing the vital signs for your product.<\/p>\n\n\n\n But soon, you’ll face more challenging questions. A key metric might suddenly drop, or a conversion rate might stagnate. Your dashboard tells you what happened, but it can’t always tell you *why* it happened.<\/strong> To truly drive growth and solve user problems, you need to move beyond simple descriptive analytics (what happened) and into the realm of diagnostic analytics<\/strong> (why it happened).<\/p>\n\n\n\n This is where true product intelligence begins, and it’s where Amplitude<\/strong>‘s more advanced features become indispensable.<\/p>\n\n\n\n This guide (Part 4 of our series) will introduce you to three of Amplitude\u2019s<\/strong> most powerful exploratory analysis capabilities: Pathfinder<\/strong>, Behavioral Cohorts<\/strong>, and Microscope (via User Look-Up)<\/strong>. We will show you how to use these specific tools to:<\/p>\n\n\n\n By the end of this post, you’ll be equipped to start uncovering the “why” behind your data and discover the unexpected growth opportunities hidden within your users’ behavior.<\/p>\n\n\n\n Let’s start with a common and often frustrating scenario for any product manager or marketer. You look at your Funnel Analysis<\/strong> chart from Part 3, and you see a massive drop-off. For example, 70% of users who performed Added Item to Cart never complete the Completed Purchase event. Your funnel chart tells you what<\/em> happened, but the critical next question is: “What did those users do <\/strong>instead<\/em><\/strong> of converting?”<\/strong><\/p>\n\n\n\n This is a question a predefined funnel, by its nature, cannot answer. To find out, you need to explore the actual, often messy, paths that users take through your product. For this, we use the Pathfinder<\/strong> chart in Amplitude<\/strong>.<\/p>\n\n\n\n What is Pathfinder? Your Product’s Real-World Map<\/strong><\/p>\n\n\n\n Think of your Funnel chart as the clean, ideal “happy path” you want<\/em> users to take. Pathfinder<\/strong>, on the other hand, shows you the real-world map of all the paths<\/em> users are actually<\/em> taking, including detours, dead ends, and unexpected routes. It creates a visual “sankey” diagram that illustrates the most common sequences of events users perform.<\/p>\n\n\n\n A Simple Walkthrough to Diagnose Funnel Drop-off<\/strong><\/p>\n\n\n\n Let’s use Pathfinder to investigate our cart abandonment problem.<\/p>\n\n\n\n The Insight (“So What?”)<\/strong><\/p>\n\n\n\n Instead of just knowing that 70% of users dropped off, Pathfinder can provide a powerful, data-driven hypothesis about why<\/em>. The chart might reveal that:<\/p>\n\n\n\n This is no longer a mystery. You now have a powerful, data-driven insight: your shipping costs or policies are likely the primary point of friction causing cart abandonment.<\/strong> This is a deep, actionable discovery that a simple funnel analysis could never provide. You can now form a clear hypothesis and run an A\/B test (e.g., offering free shipping) to directly address the problem Pathfinder helped you uncover.<\/p>\n\n\n\n In Part 3, you built a foundational Retention chart. It answered the question, “Are our new users coming back?” You likely saw an overall retention number\u2014for example, maybe 15% of your new users are still active after 30 days. This is a vital health metric, but it naturally leads to a more strategic question:<\/p>\n\n\n\n “My overall retention is 15%, but are there specific groups of users who retain at a much higher rate? What actions do my most successful, long-term users have in common?<\/strong>“<\/p>\n\n\n\n Answering this question is the key to understanding your product’s “aha!” moments\u2014the critical actions that lead to long-term value and stickiness. To do this, we need to move beyond simple acquisition cohorts (grouping users by when<\/em> they signed up) and create Behavioral Cohorts<\/strong>.<\/p>\n\n\n\n What are Behavioral Cohorts? Grouping Users by What They *Do<\/strong>*<\/p>\n\n\n\n A Behavioral Cohort<\/strong> is a segment of users defined not by time, but by the specific actions they have (or haven’t) taken within your product. This is a far more powerful way to group users for analysis, as it connects their behavior directly to outcomes like retention.<\/p>\n\n\n\n Examples of Behavioral Cohorts include:<\/p>\n\n\n\n A Simple Walkthrough to Identify a High-Value Behavior<\/strong><\/p>\n\n\n\n Let’s create a cohort to test the hypothesis that users who engage with a key collaborative feature are more likely to be retained.<\/p>\n\n\n\n The Insight (“So What?”)<\/strong><\/p>\n\n\n\n Now for the powerful part. You can use this newly created behavioral cohort as a filter across almost any other chart in Amplitude<\/strong>.<\/p>\n\n\n\n Return to the Retention Analysis chart<\/strong> you built in Part 3. Apply your new [Cohort] Power Inviters cohort as a segmentation filter. Instead of seeing the average retention for all<\/em> new users, you are now seeing the specific retention curve for only those users who invited at least three teammates in their first week.<\/p>\n\n\n\n If you see that this cohort’s 30-day retention is 50%<\/strong> instead of the overall average of 15%, you’ve just used data to identify a critical ‘aha!’ moment.<\/strong> This insight is incredibly actionable. Your product and marketing strategy can now focus on encouraging more new users to discover and successfully use the “Invite Teammate” feature, because you have quantitative proof that this specific behavior is a strong leading indicator of long-term retention and customer success.<\/p>\n\n\n\n Your analytics charts provide an essential high-level view of user behavior. You might see that 52 users dropped off your purchase funnel yesterday, or that 10% of a specific cohort churned. These numbers tell you what<\/em> happened, but they are still abstractions. What if you want to understand the real story behind those numbers? What if you could ask: “Who are some of these 52 users, and can I see <\/strong>exactly<\/em><\/strong> what they did, click by click, leading up to the moment they dropped off?”<\/strong><\/p>\n\n\n\n This is where you bridge the gap from quantitative to qualitative analysis. Amplitude<\/strong>‘s Microscope<\/strong> feature (and the related User Look-Up<\/strong> capability) allows you to do exactly this: drill down from any aggregated data point in a chart to the individual users who make up that number.<\/p>\n\n\n\n What is Microscope? From Aggregated Data to Individual Users<\/strong><\/p>\n\n\n\n Think of Microscope<\/strong> as a powerful magnifying glass for your data. It allows you to click on almost any segment of a chart\u2014a bar in a funnel, a point on a line graph, a cell in a retention table\u2014and instantly see the list of specific users who fall into that data point. This is one of the most powerful and frequently used features for root-cause analysis.<\/p>\n\n\n\n A Simple Walkthrough to Investigate Funnel Drop-off<\/strong><\/p>\n\n\n\n Let’s use Microscope to investigate the users who abandoned your purchase funnel.<\/p>\n\n\n\n The Insight (“So What?”)<\/strong><\/p>\n\n\n\n This user list is your starting point for deep, qualitative investigation. From here, you can click on any individual user profile to open their User Look-Up<\/strong> stream. This stream shows you their entire event timeline\u2014a detailed, click-by-click history of their session(s).<\/p>\n\n\n\n By examining the sessions of several users who dropped off, you might discover powerful patterns:<\/p>\n\n\n\n Microscope puts a human story behind the aggregated numbers.<\/strong> It allows you to move beyond simply knowing that<\/em> a problem exists to forming highly specific, data-backed hypotheses about why<\/em> it’s happening, enabling much more effective and targeted solutions.<\/p>\n\n\n\n By working through the analyses in this guide, you have fundamentally leveled up your analytical capabilities within Amplitude<\/strong>. You now possess a toolkit that moves far beyond simply monitoring what<\/em> is happening in your product and empowers you to begin diagnosing why<\/em>.<\/p>\n\n\n\n You have learned how to:<\/p>\n\n\n\n You have effectively made the crucial strategic shift from descriptive analytics<\/strong> (seeing the numbers) to diagnostic analytics<\/strong> (investigating the reasons behind those numbers). You can now answer not just “what happened?” but begin to truly understand “why<\/strong>.”<\/p>\n\n\n\n The Path Forward: A Continuous Cycle of Improvement<\/strong><\/p>\n\n\n\n This ability to diagnose is the true beginning of a mature, data-driven product development cycle. Your workflow can now follow a powerful loop:<\/p>\n\n\n\n This is the engine of continuous improvement.<\/p>\n\n\n\n While this guide has equipped you with powerful diagnostic tools, the next strategic step is translating these deep insights into a prioritized product roadmap and a cohesive growth strategy. If you need assistance turning advanced analytical findings into measurable business outcomes and a clear plan of action, our team at e-CENS is here to provide expert guidance.<\/strong><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" I. Introduction: From Monitoring ‘What’ to Understanding ‘Why’ In our last guide, you built your first “Core Product Health” dashboard in Amplitude. You can now see what is happening in your product at a glance: your weekly active users, your key funnel conversion rates, and your new user retention curve. This is a critical first […]<\/p>\n","protected":false},"author":39,"featured_media":35238,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","content-type":"","ub_ctt_via":"","footnotes":""},"categories":[433],"tags":[],"class_list":["post-35105","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-amplitude"],"acf":[],"featured_image_src":"https:\/\/e-cens.com\/wp-content\/uploads\/2025\/08\/Amplitude-101_-Advanced-Analysis-with-Pathfinder-Cohorts.webp","author_info":{"display_name":"Mostafa Daoud","author_link":"https:\/\/e-cens.com\/author\/daoude-cens-com\/"},"_links":{"self":[{"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/posts\/35105","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/users\/39"}],"replies":[{"embeddable":true,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/comments?post=35105"}],"version-history":[{"count":3,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/posts\/35105\/revisions"}],"predecessor-version":[{"id":35244,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/posts\/35105\/revisions\/35244"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/media\/35238"}],"wp:attachment":[{"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/media?parent=35105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/categories?post=35105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/e-cens.com\/wp-json\/wp\/v2\/tags?post=35105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
II. Uncovering Unexpected User Journeys with Pathfinder<\/h3>\n\n\n\n
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III. Building Behavioral Cohorts to Find Your Best Users<\/h3>\n\n\n\n
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IV. The Power of a Single Click: Using Microscope for Individual Deep Dives<\/h3>\n\n\n\n
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V. Conclusion: From ‘What’ to ‘Why’ \u2013 Your Analytical Superpowers<\/h3>\n\n\n\n
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